Etidronate for treating and preventing postmenopausal osteoporosis
Bibliographic record
Abstract
BACKGROUND: Osteoporosis is a clinical syndrome of reduced bone mass and increased fracture susceptibility. There are now a number of options, including etidronate which can decrease the risk of fractures. OBJECTIVES: To systematically review the efficacy of etidronate on bone density, fractures and toxicity in postmenopausal women. SEARCH STRATEGY: We searched MEDLINE from 1966 to December 1998, examined citations of relevant articles, and the proceedings of international osteoporosis meetings. We contacted osteoporosis investigators to identify additional studies, primary authors, and pharmaceutical industry sources for unpublished data. SELECTION CRITERIA: We included thirteen trials (with 1010 participants) that randomized women to etidronate or an alternative (placebo or calcium and/or vitamin D) and measured bone density for at least one year. DATA COLLECTION AND ANALYSIS: For each trial, three independent reviewers assessed the methodological quality and abstracted data. MAIN RESULTS: The data suggested a reduction in vertebral fractures with a pooled relative risk of 0.60% (95% CI 0.41 to 0.88). There was no effect on non-vertebral fractures (pooled relative risk 1.00, (95% CI 0.68 to 1.42)). Etidronate, relative to control, increased bone density after three years of treatment in the lumbar spine by 4.27% (95% CI 2.66 to 5.88), in the femoral neck by 2.19% (95% CI 0.43, 3.95) and in the total body by 0.97% (95% CI 0.39, 1.55). Effects were larger at 4 years, though the number of patients followed was much smaller. AUTHORS' CONCLUSIONS: Etidronate increases bone density in the lumbar spine and femoral neck. The pooled estimates of fracture reduction with etidronate are consistent with a reduction in vertebral fractures, but no effect on non-vertebral fractures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".